def gen_sparse_random_nan_reduction(draw, shapes, **kwargs): fraction = draw(st.sampled_from([0.25, 0.5, 0.75, 1.0])) return sparse.random( shapes, data_rvs=random_value_array(np.nan, fraction), **kwargs )
def test_all_nan_reduction_warning(reduction, axis): x = random_value_array(np.nan, 1.0)(2 * 3 * 4).reshape(2, 3, 4) s = COO.from_numpy(x) with pytest.warns(RuntimeWarning): getattr(sparse, reduction)(s, axis=axis)